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Remote Sens. 2016, 8(3), 264; doi:10.3390/rs8030264

Novel Approach to Unsupervised Change Detection Based on a Robust Semi-Supervised FCM Clustering Algorithm

1
School of Remote Sensing and Information Engineering, Wuhan University, No. 129 of Luoyu Road, Wuhan 430079, China
2
Joint Spatial Information Research Laboratory of Wuhan University and Hong Kong Polytechnic University, Wuhan University, No. 129 of Luoyu Road, Wuhan 430079, China
3
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Academic Editors: Ruiliang Pu, Parth Sarathi Roy, Heiko Balzter and Prasad S. Thenkabail
Received: 20 November 2015 / Revised: 20 January 2016 / Accepted: 11 March 2016 / Published: 22 March 2016
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Abstract

This study presents a novel approach for unsupervised change detection in multitemporal remotely sensed images. This method addresses the problem of the analysis of the difference image by proposing a novel and robust semi-supervised fuzzy C-means (RSFCM) clustering algorithm. The advantage of the RSFCM is to further introduce the pseudolabels from the difference image compared with the existing change detection methods; these methods, mainly use difference intensity levels and spatial context. First, the patterns with a high probability of belonging to the changed or unchanged class are identified by selectively thresholding the difference image histogram. Second, the pseudolabels of these nearly certain pixel-patterns are jointly exploited with the intensity levels and spatial information in the properly defined RSFCM classifier in order to discriminate the changed pixels from the unchanged pixels. Specifically, labeling knowledge is used to guide the RSFCM clustering process to enhance the change information and obtain a more accurate membership; information on spatial context helps to lower the effect of noise and outliers by modifying the membership. RSFCM can detect more changes and provide noise immunity by the synergistic exploitation of pseudolabels and spatial context. The two main contributions of this study are as follows: (1) it proposes the idea of combining the three information types from the difference image, namely, (a) intensity levels, (b) labels, and (c) spatial context; and (2) it develops the novel RSFCM algorithm for image segmentation and forms the proposed change detection framework. The proposed method is effective and efficient for change detection as confirmed by six experimental results of this study. View Full-Text
Keywords: remote sensing; unsupervised change detection; thresholding; fuzzy C-means; clustering with partial supervision; robust semi-supervised fuzzy C-means remote sensing; unsupervised change detection; thresholding; fuzzy C-means; clustering with partial supervision; robust semi-supervised fuzzy C-means
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Shao, P.; Shi, W.; He, P.; Hao, M.; Zhang, X. Novel Approach to Unsupervised Change Detection Based on a Robust Semi-Supervised FCM Clustering Algorithm. Remote Sens. 2016, 8, 264.

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